How are securities laws enforced in India: some facts from a new data-set of SEBI orders

by Devendra Damle and Bhargavi Zaveri Shah.

Introduction

The Securities and Exchange of Board of India (SEBI) is one of the most powerful regulators in India. As the regulator of one of the world's largest stock markets by market capitalization, SEBI has a variety of enforcement tools at its disposal. These include the imposition of monetary penalties, license cancellation and pursuing criminal proceedings against violators. The law empowers SEBI to issue directions to intermediaries, and more broadly, to persons associated with the securities market. Such directions may be of a prohibitory nature, such as restricting companies from raising capital in the public markets, disqualifying persons from acting on the board of publicly traded issuers and restricting access to the capital market altogether. They may also be of a remedial nature such as disgorging illegal gains made by violators or directing restitution to wronged investors. The grounds for issuing such directions are wide.

How has SEBI used these enforcement powers over time? Has it prioritized enforcement against some kinds of misconduct over others? If yes, have the priorities stayed static or changed over time? Do certain types of violations consistently entail certain types of sanctions? How efficient are the enforcement proceedings in terms of the time taken, and what is the success rate for enforcing such sanctions? Unlike some Indian financial sector regulators, SEBI follows a due process before issuing such orders, involving the issuance of a show cause notice and a hearing and publishes each enforcement order passed by its officials systematically on its website. This transparency in enforcement allows us to establish some basic facts on securities laws enforcement in India over a long observation period. In a new paper, we analyse over 8,000 enforcement orders passed by SEBI over a span of ten years to answer some of the questions we mentioned above. In this article, we summarize the key findings of our work.

Data description

In our study period beginning 1st January, 2011 and ending on 31st December, 2020, SEBI passed 9048 enforcement orders, of which we were able to sucessfully download and parse 8032 orders. We then analysed these orders, using text-mining software we designed ourselves, to arrive at some summary statistics on the frequency and type of enforcement undertaken by SEBI during the study period. To answer more detailed questions on the nature of enforcement, we manually analysed a stratified random sample of about 10% of these orders. The sample was drawn from the set of orders involving four regulations, which are most frequently enforced by SEBI (as per our data), namely, orders pertaining to fraudulent and unfair trade practices in the Indian securities market (FUTP), violations of the Insider Trading regulations, the Takeover Code and Broker regulations.

As mentioned above, the SEBI Act empowers SEBI to pass two types of orders, namely, orders imposing monetary penalties and orders issuing directions. Such orders can be issued against intermediaries, market participants, issuers of capital or persons generally associated with the securities market. Until 2019, monetary penalty orders could be passed only by adjudication officers and directions would be issued by whole time members of the SEBI board. With effect from 2019, the members of the SEBI board have also been empowered to pass orders imposing monetary penalties. In addition to these, the law also empowers SEBI to settle violations upon the payment of a settlement fee, without passing a guilty verdict against the violator. Basis this scheme of the SEBI Act, we categorize the enforcement orders in our data set into three categories shown in the Table. On an average, SEBI issues 250 enforcement orders with directions and double the number of orders imposing monetary penalties each year. The SEBI Act also empowers SEBI to initiate criminal prosecution against persons accused of having violated the SEBI Act or the regulations made by it, but we do not take account of this typology of enforcement proceedings in our study.

Table: Enforcement orders (2011-20)

Type of order	Type of sanction	Total^
Orders by Adjudicating officers	Monetary penalties	4911 (61)
Orders by Chairperson/member	Non-monetary sanctions	2484 (31)*
Settlement orders	Settlement fee	637 (8)
Total		8032 (100)
^Numbers in brackets are a percentage of the total. *We estimate that not more than 30 orders may involve a monetary penalty.

As is evident from the Table, securities law enforcement is largely undertaken in India through monetary penalties, but the proportion of enforcement undertaken through non-monetary sanctions is not trivial. Settlements account for less than 10% of the total enforcement orders in our data. The annual distribution of these types of orders is shown in the Figure. The Figure shows that from 2018 onwards, there has been a sharp increase in the intensity of enforcement, with the number of monetary penalty orders nearly doubling from the previous years. The proportion of settlements has also increased over time, particularly after 2016. While the growth in the size of the market, an increase in the intensity of regulation and enforcement capacity are intuitive explanations for this jump, more precise, causal explanations require further research.

Figure: Year-wise types of enforcement orders (2011-2020)

Findings

SEBI draws its substantive powers from a set of three laws, over and above the SEBI Act, namely, the Companies Act, 2013 (and its preceding legislation), the Securities Contracts (Regulation) Act,1956 (SCRA) and the Depositories Act, 1996. While the Companies Act largely deals with the incorporation of Indian companies and the governance of their affairs, it also governs primary issuances, the requirements to be met by public offer documents and some aspects of the governance of listed companies. These matters under the Companies Act are administered by SEBI. The SCRA governs the conceptual definition of securities and securities contracts, regulates some types of securities contracts and governs the licensing and affairs of stock exchanges. The Depositories Act, 1996 deals with the regulation of depositories and depository participants. Under each of these laws, and in particular under the SEBI Act, SEBI has issued regulations defining the registration and reporting requirements for intermediaries, the kinds of misconduct that will elicit penalties, and so on.

We find that orders against fraudulent and unfair trade practices (FUTP) are the single largest group (15%), followed by orders dealing with violations of the provisions of the Companies Act (11%), insider trading regulations (10%) and the takeover code (9%). The enforcement actions (i.e. the number of orders) under the remaining regulations are few, with some of them having witnessed enforcement not more than once during the study period. Some of these seemingly rarely-enforced regulations, such as the regulations governing alternative investment advisors, are relatively new, which may explain why they do not appear more often in our data. However, others, such as the regulations governing venture capital funds, stock exchanges and clearing corporations, are older, but we see fewer orders issued under these regulations as compared to other regulations. Whether this is because the regulations themselves are not violated as frequently by market participants, or because SEBI chooses not to enforce them, requires further study.

To answer more specific questions of these enforcement orders, we manually analysed a random sample of 818 orders (approximately 10% of the total sample) from amongst the orders against the following types of violations: (1) FUTP, (2) insider trading, (3) violations of the takeover code and (4) violations of brokers' regulations. Some findings from this micro-study are summarised below:

1. Duration of the enforcement proceedings: The formal enforcement process at SEBI begins with the appointment of an investigating authority who investigates the facts and reports her findings to the SEBI board. If the findings are adverse, a show cause notice is issued to the accused by the adjudication officer (where the proposed sanction is a monetary penalty) or a whole time member of the SEBI board (where the proposed intervention is a direction). We find that the median time for the issuance of a show cause notice is a little more than three years from the date on which the violation was committed. Further, the median time from the date of issuance of a show cause notice to the date of an order imposing monetary penalties is a year and a half. It is a little more than two years for orders issuing directions. A regulation-wise analysis of the duration suggests no relationship between the complexity of the violation involved and the duration of the enforcement proceeding.
2. Subject and outcome of enforcement: A bulk of the enforcement actions are in respect of unregulated entities, that is, entities that are not SEBI-licensed intermediaries. This phenomenon could be attributed to the type of violations that are most often enforced against, namely FUTP and insider trading. Both these practices would likely involve traders and market participants that are not SEBI-licensed intermediaries. Further, in nearly 80% of the cases, SEBI found the person(s) guilty of all the violations that they were charged with, with a marginally higher conviction rate for unregulated entities compared to regulated entities. The conviction rate for violations of the Takeover Code is also marginally higher, compared to violations under the three sets of regulations. It is hard to comment on the optimality of this high conviction rate as these enforcement proceedings are undertaken and decided by SEBI officers themselves. All orders of SEBI, except those rejecting an application for settlement, are appealable to the Securities Appellate Tribunal (SAT). The rate of appeals and the outcome of appeals before the SAT could be a rough proxy to evaluate the optimality of this conviction rate and would be a good direction for further research.
3. Proportionality of sanction: We find a lot of variation in the amount of penalty levied across cases. While the median (i.e. typical) size of the penalty is in the range of Rs 5,00,000, the average is in the range of Rs. 57,00,000. This difference indicates that while there are few cases where large penalties are issued, the size of these penalties is very large compared to the typically-imposed penalties. One explanation that could account for this variation is the amount involved in the violation. The SEBI Act requires an Adjudicating Officer to take into account, among other factors, the amount of disproportionate gain or unfair advantage made as a result of the default or the amount of loss caused to investors as a result of such default. However, we find that in a vast majority of the cases in our sample (90%), the size of the violation was not calculated.

   We similarly find a lot of variation in the orders that impose sanctions other than monetary penalties. Out of 118 such orders, 82 orders restricted the market access of the accused. The duration of such restrictions varied from 15 days to 4 years, and we could not discern any relationship between the duration of the restriction on the one hand and the violation or the purpose of the restriction on the other. Further, courts have repeatedly held that SEBI's direction making powers are remedial and preventive in nature, and not punitive. However, it is unclear at what point an order that operates to restrict market access starts to become punitive in nature, since none of the orders in our data clearly draw the line between remedial and punitive measures.

Conclusion

In India, the field of securities laws is often studied from the perspective of a specific case, individual legislative amendments or specific judgements of courts. While such analysis is useful, a slightly different, more quantitative approach is necessary to gain a systematic understanding of the manner in which the regulator uses the wide variety of enforcement tools available to it, the manner in which it seeks to enforce against different kinds of misconduct and the efficiency of its enforcement functions. The consistent publication of easily accessible enforcement orders by SEBI on its website makes it possible to undertake such systematic research on securities laws enforcement in India. This paper is one such effort to begin developing more systematic knowledge on enforcement of private law in India.

The data used for this analysis can be found here. The data-set can be cited as Zaveri Shah, Bhargavi; Damle, Devendra (2022), "Securities law enforcement in India", Mendeley Data, V1, doi: 10.17632/ppdk9pzfdp.1.

Devendra Damle is an independent researcher. Bhargavi Zaveri Shah is a doctoral candidate at the National University of Singapore.

How large is the payment delays problem in Indian public procurement?

by Pavithra Manivannan and Bhargavi Zaveri.

Payment delays are endemic in government contracts in India. Businesses generally factor payment delays into the price of public sector contracts. Measuring the size and extent of overall payment delays from the government to vendors and contractors has, however, been a challenge. In this article, we use a novel data-set put together from public sources to ascertain the size of the payment delays problem in Indian public procurement.

When a private entity delays contractual payments, the delay is factored into the price of the next vendor contract or the debt contracted by the private entity. This feedback loop naturally instills payment discipline by aligning the payer's incentives with maintaining payment discipline. This is harder to achieve for government contracts, as information about payment delays in public procurement is often sparse, difficult to discern from budgetary statements or missing altogether. The problem is compounded as the state procures goods, services and works at various levels and through various entities owned by it. Payment delays affect the working capital cycle of vendors of all sizes. However, payment delays have a particularly deleterious impact on Micro Small and Medium Enterprises (MSMEs), which often have limited access to formal financial systems to bridge their working capital requirements. Timely payments, therefore, are of crucial importance to MSMEs as they rely on their cash-flow cycle to fund their working capital requirements.

Payment delays in contracts with CPSEs

A significant proportion of overall central government procurement is undertaken by centrally owned public sector enterprises (CPSEs). Most CPSEs are incorporated as companies and many of them are listed. We use the information in the annual results of CPSEs as a proxy to ascertain the scale of payment delays in the public procurement undertaken by the central government. We study the balance sheet and annual reports of listed CPSEs for the last three financial years, 2017-18, 2018-19 and 2019-20 ("study period").

We find that CPSEs had annual average outstanding trade payables of Rs.1.3 trillion as against an annual average procurement value of Rs.1.1 trillion, during our study period. This suggests that the annual average outstanding trade payables of CPSEs were about 18% higher than the annual average procurement undertaken by the CPSEs. We also find that when taken as a percentage of the value procured, CPSEs under some ministries, such as the Railway and Defence Ministries and Ministry of Housing and Urban Affairs, fare significantly worse than others. Further, we find that on an average, payments worth 8% of the total value procured from MSMEs are delayed for more than 45 days from their due date. Finally, we find that CPSEs demonstrate weak payment discipline towards all their vendors, and that the MSME vendors are not worse-off than the other vendors. This suggests that in the case of government contracts, the imbalance of the relative negotiating power of MSME vendors and non-MSME vendors has limited impact on the behaviour of the payer.

Our work demonstrates the potential to develop an ongoing system to measure payment discipline in public procurement, which could then act as a feedback loop for pricing vendor contracts when dealing with CPSEs and the government departments to which they are aligned.

Data and methodology

Our analysis is based on a hand collected data-set put together from the following two public sources of data:

1. the MSME Sambandh portal set up by the the Ministry of Micro, Small and Medium Enterprise in 2017, to monitor the implementation of the Public Procurement Policy, 2012;
2. Annual reports and annual balance sheets published by CPSEs.

Our data-set consists of firm level information about CPSEs, such as the year of their incorporation, listing date, industry classification, variables indicative of their financial health and the procurement undertaken by them. We augment the data-set with information on payments delayed by CPSEs to MSME suppliers beyond 45 days from the date on which such payments became due (hereafter, "delayed payments"). The Micro, Small and Medium Enterprises Act, 2006 (MSME Act)requires all companies that procure goods, services and works from micro, small and medium enterprises to disclose such payments in their annual report in the prescribed format.

Our data-set covers this information for 57 listed CPSEs. We collect the data for these CPSEs for three financial years beginning with the year in which the MSME Sambandh Portal was set up. This gives us data for the financial years, 2017-18, 2018-19 and 2019-20, which is our study period.

These 57 CPSEs are spread across 17 departments or ministries of the Central Government, with the largest number concentrated under the Ministry of Petroleum and Natural Gas (19.3%), followed by the Ministry of Power (10.53%) and the Ministry of Steel (8.77%). The CPSEs in our data-set are spread across 34 industries, as per the National Industrial Classification (NIC) scheme prescribed by the Ministry of Statistics and Programme Implementation (MoSPI). CPSEs in the business of 'electricity, gas, stem and hot water supply' account for the largest group (12.28%) followed by CPSEs engaged in the business of manufacturing coke, refined petroleum products and nuclear fuel (8.77%).

Each CPSE reports a target procurement value at the beginning of the financial year and the actual procurement value at the end of the financial year, on the MSME Sambandh portal. Table 1 shows the aggregate value of goods, services and works targeted and actually procured by the CPSEs in our data-set across different government departments.

Table 1: Procurement by CPSEs

	No. of CPSEs	Target (Rs. crore)	Actual (Rs. crore)
Department of Chemicals and Petrochemicals	2	371.66	308.38
Department of Commerce	2	17.28	18.3
Department of Defense Production	3	1963.33	2880.47
Department of Fertilisers	4	2547.34	2641.74
Department of Heavy Industry	4	16392.1	15209.25
Department of Telecommunications	2	0	0
Ministry of Coal	2	5977.39	1774.82
Ministry of Defense	3	5838.79	7066.05
Ministry of Housing and Urban Affairs	2	12.61	12.67
Ministry of Mines	2	2573	7132.09
Ministry of Petroleum and Natural Gas	11	52175.63	63849.08
Ministry of Power	6	6142.97	6608.55
Ministry of Railways	4	395.07	429.31
Ministry of Science and Technology	1	38	17.49
Ministry of Shipping	3	1725.67	1321.45
Ministry of Steel	5	4556.4	5356.22
Ministry of Tourism	1	125.07	34.85

The Ministry of Petroleum and Natural Gas is the largest procurer in our data-set, both in terms of the number of CPSEs and the value of goods, services and works procured by them. Table 1 also shows that a majority of the CPSEs have procured more than their annual targeted value. We observe this to be true across all the three financial years comprised in our study period.

Findings: CPSEs' outstanding dues

Trade payable are a rough proxy of the amounts due from a firm to vendors and service providers. We use the data on outstanding trade payable from the balance sheets of CPSEs as an estimate of payment delays in public procurement. Table 2 shows the three-year annual average outstanding trade payable due from the CPSEs in our data-set. The second column indicates the corresponding annual average value of procurement undertaken by these CPSEs, and the last column indicates the average outstanding trade payable as a percentage of the average annual procurement undertaken by the CPSEs.

Table 2: Average outstanding trade payable and procurement value

	Procurement value (Rs. crore)	Outstanding payable (Rs. crore)	Payable/ procurement (percent)
Department of Chemicals and Petrochemicals	308.38	106.11	34.41
Department of Commerce	18.3	1119.66	6118.36
Department of Defense Production	2880.47	3449.92	119.77
Department of Fertilizers	2641.74	1971.93	74.65
Department of Heavy Industry	15209.25	10297.54	67.71
Department of Telecommunications	0.00	2313.67	0.00
Ministry of Coal	1774.82	1898.59	106.97
Ministry of Defense	7066.05	3031.90	42.91
Ministry of Housing and Urban Affairs	12.67	2709.40	21384.37
Ministry of Mines	7132.09	1227.54	17.21
Ministry of Petroleum and Natural Gas	63849.08	82830.09	129.73
Ministry of Power	6608.55	7907.65	119.66
Ministry of Railways	429.31	1243.77	289.71
Ministry of Science and Technology	17.49	29.38	167.98
Ministry of Shipping	1321.45	1562.02	118.21
Ministry of Steel	5356.22	8739.99	163.17
Ministry of Tourism	34.85	58.94	169.12
Total	114660.72	130498.11	113.81

While the procurement value of a given financial year does not necessarily mean that the entire value of the contract becomes payable in the same financial year as the procurement contract may span across multiple years, the three year average numbers in Table 3, however, show a systemic break-down in the payment discipline of CPSEs. We speculate that these trade payable would have aggregated over time, and do not necessarily pertain entirely to the study period.

We then look at three departments/ ministries that account for the largest procurement by value in our data-set, to investigate the differences in the payment behaviour of CPSEs towards MSMEs and non-MSME vendors (Table 3). The CPSEs in these three departments also account for nearly 75% of the total outstanding trade payable of all the CPSEs in our data-set.

Table 3: Outstanding trade payable of CPSEs (as percent of value procured)

	2017-18		2018-19		2019-20		Total
	Non-MSME	MSME	Non-MSME	MSME	Non-MSME	MSME	Non-MSME	MSME
Department of Heavy Industry	84.02	7.87	80.87	15.82	110.63	11.81	88.89	12.22
Ministry of Petroleum and Natural Gas	182.65	7.60	187.97	3.56	143.05	4.13	171.49	4.32
Ministry of Power	116.44	14.90	161.54	15.35	292.02	21.73	175.63	17.46

In each of the three cases, the proportion of total outstanding trade payable to the value procured by the CPSEs during the study period is much higher for non-MSMEs than MSMEs. In the case of the Ministry of Petroleum and Natural Gas and the Ministry of Steel, the proportion of outstanding trade payable to non-MSME vendors exceeds 100% of the value procured, on an aggregate basis across the three years. Compared to non-MSME vendors, this proportion is significantly lower for MSME vendors (the highest being 21%).

Findings: Delayed payments by CPSEs to MSME suppliers

The final leg of our measurement involves estimating the 'delayed payments' by CPSEs to MSMEs, that is, payments delayed beyond 45 days from their due date. We aggregate the delayed payments outstanding as at year-end, by CPSEs to MSMEs, government department wise. Table 4 shows the delayed payments as a percentage of their annual procurement value from MSMEs.

Table 4: Delayed payments as percentage of procurement

	2017-18	2018-19	2019-20	Average
Department of Chemicals and Petrochemicals	6.22	11.73	11.84	10.46
Department of Commerce	0.86	47.38	1.08	17.47
Department of Defense Production	5.82	3.35	5.44	4.78
Department of Fertilizers	7.58	5.25	4.82	5.38
Department of Heavy Industry	7.91	15.92	12.11	12.37
Department of Telecommunications	0	0	0	0
Ministry of Coal	9.11	5.42	8.47	7.19
Ministry of Defense	3.55	3.20	3.98	3.56
Ministry of Housing and Urban Affairs	6.72	4.86	5.49	5.70
Ministry of Mines	2.79	1.51	2.85	2.37
Ministry of Petroleum and Natural Gas	4.12	4.80	6.18	5.25
Ministry of Power	24.95	25.33	31.87	27.51
Ministry of Railways	11.03	20.58	11.02	13.62
Ministry of Science and Technology	0	0	0	0
Ministry of Shipping	14.36	3.47	2.94	5.62
Ministry of Steel	4.84	10.18	4.81	6.18
Ministry of Tourism	0	0	21.13	21.13
Total	7.36	8.06	8.70	8.16

We find that the delayed payments by CPSEs to MSMEs average at about 8% of the actual value of goods, services and works procured by them from MSMEs during the study period. This percentage has marginally increased from 2017-2018, and is higher than the average in 2019-20.

Conclusion

In this article, we take a sector-agnostic approach to measure the scale of the payment delays problem in public procurement in India. An analysis of the annual returns and balance sheets of CPSEs gives us new insights on the scale of the problem at three levels, namely, at the level of the CPSE, the industry and the government department to which the CPSE is aligned.

Our analysis provides evidence of the popular perception of CPSEs' weak payment discipline to vendors. Taken as a percentage of the average procurement undertaken by CPSEs, the payment delays by CPSEs to their vendors far exceeds their procurement values. Second, while CPSEs demonstrate weak payment discipline to both MSME and non-MSME vendors, the delay seems to be much larger towards large vendors than the small ones. Third, the delayed payments reporting requirements mandated under the MSME Act provides us an illustrative picture on the payment discipline of the CPSEs. For two out of three years of our study period, we notice that the total delayed payments to MSMEs is higher than the three-years average (Rs. 2323.42 crores).

Our approach of understanding payment delays in public procurement in India, through balance sheets of CPSEs demonstrates the possibility of setting up ongoing systems for the measurement of payment discipline of government departments through CPSEs aligned to them. Further, these delays may be indicative of either of liquidity mismatches or solvency issues, at the CPSE, or a mix of both. By approaching this problem from a balance sheet perspective, our study lays the foundation for conducting future work on the possible relationship between the financial health of the procurers and their payment discipline.

The authors are researchers at the CMI-Finance Research Group and thank Susan Thomas for valuable discussions.

Does India need a public procurement law?

by Shubho Roy and Diya Uday.

One of the proposed solutions to India’s public procurement problems is new legislation to govern how the government buys goods and services from the private sector. Will a law help India? We connect two data sources to test this idea. Instead of a new law, monitoring public procurement, identifying failures, and then building state capacity may be a better solution. Legislation may not be the silver bullet for our problems.

In India, legislation is often viewed as a panacea when faced with policy problems. Whether it is bankruptcy, privacy, warehousing, or medical testing in private laboratories; the government is quick to propose a new law to solve problems. The same approach has been attempted to address the issues of public procurement. In 2012, the government introduced the Public Procurement Bill with the stated reason as:

“Major countries of the world have well codified legal provisions governing public procurement.” (Statement of Objects and Reasons).

An international organisation also prescribed this solution for India. In 2013, the United Nations Office on Drugs and Crime recommended that India should enact the Public Procurement Bill. According to the UN agency, the bill would improve public procurement and reduce corruption. The bill lapsed, and the government changed. However, the idea that a law is needed persists. The present government had plans for introducing similar legislation. In the 2015-16 budget speech, the finance minister stated:

“Malfeasance in public procurement can perhaps be contained by having a procurement law and an institutional structure consistent with the UNCITRAL model. I believe Parliament needs to take a view soon on whether we need a procurement law, and if so, what shape it should take.” (Paragraph 72)

The present government is yet to introduce a bill.

It seems intuitive that a better law should improve public procurement. More transparent systems that make procurement information widely accessible and encourage more firms to participate, deter kickbacks and other forms of fraud and corruption (Ware et al.). Countries with legal provisions which discourage governments from closing bids to select vendors or establish an independent dispute resolution mechanism seem to have less bribery of public officials (Knack et al.). However, better laws may not necessarily result in better outcomes (Sukhtankar and Vaishnav and Bosio et al.). In this article, we look at the correlation between the state of the procurement law in a country and the outcomes from public procurement.

Parliamentary laws and corruption outcomes

The first step towards measuring the outcome is to agree on metrics of the quality of public procurement. The quality of a procurement law/system may be determined by multiple variables such as the conservation of public resources, purchase of better products, timely payment to vendors and integrity. However, we do not have data to measure these. We suggest an interesting proxy that we do observe: corruption perception. The predominant form of corruption, in most countries, is corruption in public procurement. Therefore, one of the primary objectives of making a public procurement law is to reduce corruption. We hypothesise:

If adopting a law improves public procurement, we should see lower corruption in those countries.

To examine this evidence, we look at two databases: Benchmarking Public Procurement and, Corruption Perception Index.

1. World Bank’s 2017 Benchmarking Public Procurement Database(BPP). This is a comparative evaluation of the legal systems governing public procurement in 180 countries (World Bank BPP, 2017). Experts analyse the laws governing public procurement on eight criteria. The criteria start from the preparation before a tender is published and extend to dispute resolution and complaint management systems. Economies with more extensive legal frameworks score higher on the BPP than countries with less comprehensive legal frameworks for public procurement. In this sense, the BPP measures the extent to which a country has accepted and implemented the idea that a better law for public procurement is desirable.
   
2. Transparency International’s Corruption Perception Index (CPI). Transparency International scores jurisdictions based on the perception of corruption in a country’s public sector. It is based on opinion polls and surveys across countries. Low scores mean higher corruption and higher scores imply high government integrity.

We look at the correlation between the World Bank’s BPP score and the Corruption Perception Index. We collected BPP data for 2017 and the CPI data for 2019 (latest years). We narrowed down the countries present in both databases, which yields information about 163 of 180 countries (91.12% of the datasets).

Findings

As Figure 1 shows, We find no correlation between the BPP scores and the CPI scores of countries. It is particularly interesting to look at the countries where the two run in different directions. Italy and Kazakhstan have very similar BPP Scores (79.33 and 79.50) but very different CPI Scores (34 and 53). China has a much higher BPP score than Hong Kong (74.66 against 48.66), but in CPI scores, China does significantly worse than Hong Kong (41 and 76). India (61.50), Australia (60.83), and Singapore (60.50) have very similar BPP Scores, but very different CPI scores (41, 77, and 85, respectively). Russia is 14 points ahead of the United Kingdom in the BPP but significantly behind on the CPI by 49 points.

Figure 1:Quality of Law and Corruption

Similarly, as Table 1 shows, the Bahamas, Hong Kong and Barbados rank quite high on the CPI (little corruption) but do quite poorly on BPP ranks. On the other hand, Kazakhstan, Congo and Yemen have high corruption (low CPI score) but score higher on the BPP.

Table 1: Comparing Rankings

Country	CPI score	BPP score	CPI Rank	BPP Rank
Barbados	62	40.20	30	157
Hong Kong	76	48.66	16	141
Bahamas	64	44.66	29	151
Kazakhstan	34	79.50	104	2
Congo	18	64.33	155	43
Yemen	15	64.66	162	41

This evidence is consistent with the arguments by Sukhtankar and Vaishnav and Bosio et al. that better laws do not correlate with better outcomes in public procurement.

What might be going on?

Why is there no correlation between corruption and quality of public procurement laws? Two reasons may explain our observations: isomorphic mimicry or imperfect measurement.

Isomorphic mimicry: ‘Isomorphic mimicry’ is the ability of organisations to sustain legitimacy through the imitation of the forms of modern institutions, but without functionality (Andrews et al.). Countries may adopt laws and institutions which are considered global best practices. However, the laws are not enforced, and the institutions are ineffective. One of the reasons for the observed results could be that countries are adopting law intending to score high on an international indicator without the requisite state capacity or active institutions to implement such a law. While this creates the facade of a sound legal system, the on-ground reality is quite different. International aid agencies sometimes require that a country have a sound legal system for public procurement, where superficial measures such as passing a law are considered sufficient. A government trying to attract international donors might pass `modern’ legislation to showcase or appeal to donors, foreign academics, journalists or NGOs. However, the government may have no intention or capacity to implement the law.

Imperfections in the BPP: The BPP as a measure appears to have a sensitivity problem. The OECD has overarching public procurement guidelines with which all members have to comply. We should, therefore, see OECD countries cluster towards the higher end of the CPI and BPP scores. While this holds for CPI scores, it does not, for BPP. BPP scores of OECD show much more variance than their CPI scores. The fact that OECD countries have adopted a common framework on public procurement appears to be not captured by the BPP measurement system.

The BPP may fail in measuring the quality of procurement laws in a country because of invisible infrastructure. Invisible infrastructure is the superset of general laws, institutions and accountability arrangements in the country which are crucial for determining the success of specific policy intervention (Kelkar and Shah). A common law country like the UK may have binding precedents setting transparency and accountability standards but may not have legislation. Constitutional provisions governing equality before the law or requiring due process apply to government procurement. Freedom of information laws may bring about transparency generally and may apply to procurements. Governments may have general laws which require government agencies to appoint an ombudsman or inspector general. Such offices may take active steps to reduce corruption and settle procurement disputes. However, such rules are not captured in a measurement system like the BPP as it is limited to government procurement legislation (Bosio et al.) The elements of invisible infrastructure may suffice, in itself, to generate high-quality procurement absent a law, and invisible infrastructure may matter in shaping the consequences of any procurement law. In either event, by focusing on the procurement law we tend to not notice the binding constraint, the invisible infrastructure.

Looking ahead

Before making laws, we need to identify the causes of the poor performance of public procurement in India. We have a history of failing in implementation and monitoring in India. Both require robust, invisible infrastructure which is missing. The first step is to build the load-bearing capacity of the procurement system. Pritchett et al. point out that premature load-bearing arising from unrealistic expectations about the level and rate of improvement of the ability of a state lead to stresses and demands on systems that cause capability to weaken if not collapse.

Two websites which aggregate procurement across government departments may provide clues on how to improve state capacity. The Government E-Marketplace (GEM) and the Central Procurement Portal (CPPP), operated by the central government, aggregate and standardise procurement notices across various government bodies. These websites aid the procurement process in many ways. Tenders are made public on a common portal instead of being scattered across multiple publication sources. This increases competition as bidders are less likely to miss a tender because they do not buy a specific newspaper. The method of tender publications is standardised, and this helps bidders apply for tenders with lesser effort. Moving away from paper-based systems reduces the chance of bids getting lost.

The more significant benefit from these websites is that they allow the government to measure/monitor the quality of the procurement process (outcome measurement) across multiple variables. This is better than measuring the quality of some legislation (input measurement) of BPP. The CPPP website publishes 16 performance indicators derived from the transactions carried out on the site. For instance, in 2019-20, 23% of the open tenders were not awarded within the bid-validity period. i.e. the buyer did not finalise the transaction in time. Sadly, most of the performance indicators tracked by the CPPP website, since 2016, show no discernable trends that procurement performance is improving.

Other jurisdictions have implemented interventions, similar to the performance indicators in the CPPP website, to improve public procurement system. The Government Accountability Office of the U.S. publishes performance reports on government procurement (which does worse than Kazakhstan on the BPP Score). Instead of legislating, India may benefit from looking at the performance indicators on the CPPP website and working on improving them every year.

We should not be lured by silver bullets, such as enacting legislation. While legislation has a role to play in governance, the evidence indicates that it is not a panacea for our problems. Some countries with good outcomes do not necessarily have an extensive legal framework for public procurement. Some nations with comprehensive laws continue to demonstrate poor results. The pathway to a better procurement system perhaps lies in detailed research that integrates public administration, law and public economics.

References

Erica Bosio, Simeon Djankov, Edward L. Glaeser, Andrei Shleifer, Public Procurement in Law and Practice. National Bureau of Economic Research, May 2020

Matt Andrews, Lant Pritchett, Michael Woolcock, Looking Like a State: Techniques of Persistent Failure in State Capability for Implementation, CID Working Paper No. 239 June 2012.

OECD, OECD Foreign Bribery Report: An Analysis of the Crime of Bribery of Foreign Public Officials, OECD Publishing, 2014

Sandip Sukhtankar, Milan Vaishnav, Corruption in India: Bridging Research Evidence and Policy Options, India Policy Forum 2014-15: Volume 11, April 2015

Stephen Knack, Nataliya Biletska, Kanishka Kacker, Deterring Kickbacks and Encouraging Entry in Public Procurement Markets, Development Research Group, World Bank, May 2017

Tina Søreide, Corruption in public procurement Causes, consequences and cures, Chr. Michelsen Institute of Development Studies and Human Rights, 2002

United Nations Office on Drugs and Crime, India: Probity in Public Procurement, 2013

Vijay Kelkar, Ajay Shah, In Service of the Republic: The Art and Science of Economic Policy, 2019

Ware, Glenn T., Shaun Moss, J. Edgardo Campos, and Gregory P. Noone, Corruption in Public Procurement: A Perennial Challenge in The Many Faces of Corruption Tracking Vulnerabilities at the Sector Level - Handbook of Global Research and Practice in Corruption, Washington, DC, The International Bank for Reconstruction and Development, 2007

World Bank, Benchmarking Public Procurement - Assessing Public Procurement Regulatory Systems in 180 Economies, World Bank Group, 2017

Shubho Roy is a researcher at the University of Chicago. Diya Uday is a senior researcher at the Finance Research Group, Mumbai and visiting faculty at the Tata Institute of Social Science, Mumbai.

The five paths of disinvestment in India

by Sudipto Banerjee, Renuka Sane and Srishti Sharma.

Privatisation of Central Public Sector Enterprises (CPSEs) in India has typically been done in one of the following ways: in the early years government equity was sold through an auction to financial investors, while since 2004, the popular method has been public offer. Strategic sales, where control of the public sector is transferred to private entities have been very few, concentrated in the 1999-2004 period. As a result, sale of government shareholding in India is referred to as disinvestment and not privatisation.

In recent years the methods used for disinvestment include: a) Public offer, b) Buybacks, c) Sale to employees, d) Exchange traded funds (ETFs), and e) CPSE to CPSE sale. Buybacks and ETFs are new ways of divesting minority stake. As we study the trajectory of disinvestment in India, it is important to understand the relative magnitudes involved in each transaction. There are two metrics that are important - first, the amount of resources raised and second, the change in government equity through these methods. The latter is especially important as disinvestment has great potential to improve economic efficiency by reducing government control. By focusing only on resources raised as an outcome, we end up ignoring the more important economic rationale for undertaking disinvestment.

In this article, we describe the methods adopted for disinvestment of CPSEs since FY2015. We use the BSEPSU disinvestment database and individual annual reports of firms to arrive at the magnitudes of disinvestment. We use two measures:

• Disinvestment proceeds and shares sold. The proceeds are the amount realised through the sale process. Shares sold is the ratio of the number of government shares sold by the total equity of the firm.
• Change in government equity. This is the difference between the share of government in total equity of the firm before and after the disinvestment transaction.

Disinvestment methods

Table 1 provides an overview of disinvestment by the government in the last 6 years. It shows the number of transactions, the number of CPSEs, the disinvestment proceeds, % of total shares sold and the change in government equity post the transaction.

Table 1: Disinvestment from FY15 to FY20

	Methods of disinvestment	Number of transactions	Number of CPSEs	Disinvestment proceeds (INR million)	Average % of shares sold	Average change in % of govt equity post disinvestment
1	PUBLIC OFFER	37	32	984,054	10	10
2	BUYBACK	36	23	403,549	8.34	0.64
3	SALE TO EMPLOYEES	21	15	9,379	0.138	0.138
4	EXCHANGE TRADED FUND*	10	18	989,490	1.09#	1.09#
5	CPSE TO CPSE SALE	8	8	667,119	77.15	77.15
Source: BSEPSU database and authors' calculation based on annual reports

* There were a total of 10 tranches of ETFs in this period. Each tranche contains a basket of firms. If the disinvestment in each firm that was part of an ETF tranche is considered separately then we would have 126 ETF transactions instead of 10. The average change in government equity for ETFs is therefore calculated across these 126 transactions, and not the 10 tranches

The government of India disinvested its stake in 50 CPSEs and raised a total of INR 3,053 billion using five methods: public offer, buy back, CPSE to CPSE sale, exchange traded funds and sale to employees. On an average, the government sold 7.28% of total shares and the average reduction in government equity has been around 5.84%. The sum total of the number of CPSEs in column 2 does not match with the total number of 50 unique CPSEs because some CPSEs adopted multiple methods across years. Public offer was the most used method with 32 firms and 37 transactions. The second most popular method was buyback with 36 transactions. The maximum revenue was raised through ETFs followed by public offer. The maximum share of sales and change in government equity was through CPSE to CPSE transfers. There is some missing data on % shares sold for buyback and ETF transactions as annual reports for FY20 is not published yet (indicated by #).

Figure 1 below shows the yearly distribution of amount raised and % reduction in equity across various methods from FY15 to FY20. The significant increase in proceeds in FY18 and FY19 is driven by ETFs and CPSE to CPSE sales. Besides CPSE to CPSE sales, the average % reduction in government equity remained low and constant across all years. We next study the five methods in detail and understand the extent of disinvestment in each method.

Public offer

Public offer has been the most common method of disinvestment. Since FY 2015, there have been 37 public offer transactions including 21 offer for sale (OFS) transactions. The public offer route is considered as a transparent way of offloading government shares and aims to encourage public participation. In several public offer transactions, the Life Insurance Corporation (LIC), whose shares are fully owned by the central government, has bought majority of the shares. Some of these transactions include:

1. In 2014, LIC bought 5.94% stake in Bharat Heavy Electricals Ltd (BHEL) for INR 26,850 million, increasing its stake in BHEL to 14.99%.
2. In 2015, LIC bought shares worth INR 70,000 million INR in the public offer of Coal India Ltd. This was equivalent to one-third of the public offer.
3. In the same year, it bought nearly 86% of the shares on offer of the Indian Oil Corporation paying over INR 80,000 million.
4. In 2016, LIC bought 59% of shares offered in NTPC stake sale worth $730 million. Thus, LIC spent approximately INR 29,000 million.
5. In 2017, LIC bought shares worth around INR 80,000 million in the disinvestment of General Insurance Corporation of India and again bought shares worth INR 65,000 million in the IPO of New India Assurance Company.
6. In March 2018, LIC subscribed 70% of shares in the IPO of Hindustan Aeronautics Ltd, paying INR 29000 million.
7. Between November 22, 2019 and February 27, 2020, LIC acquired 59.49 lakh shares worth INR 1,770 million, or 2.38 % stake, in RITES though an offer-for-sale (OFS).

LIC spent roughly INR 381,620 million on the transactions listed above. This constitutes 38.7% of the disinvestment proceeds raised through the public offer method in the period of our study.

Buyback

Buyback is a process where a company purchases its shares from its existing shareholders. This helps a company to restructure capital and increase the underlying value of shares. The company is required to extinguish the bought back shares. The government has used buyback in the past as a method of disinvestment. However in 2016, buyback was made compulsory for CPSEs who met the prescribed threshold of net worth and cash reserves.

A company is under an obligation to provide a buyback offer to all existing shareholders. In such a case, reduction in the total equity is higher than the reduction in government shares which may lead to an increase in % of government equity post buyback. However, if a CPSE is wholly owned by the government, total number of shares will be reduced (extinguished) by the same number of shares bought back. Hence, there will be no change in % of equity held by the government post buyback.

Table 2 presents the impact of buyback transactions on government shareholding. Since 2015, 23 CPSEs have bought back shares from the government raising INR 403,549 million. It is important to note that % shares sold for three buyback transactions in FY20 is unavailable since annual report for the year is not published yet (indicated by *). Out of total 36 buyback transactions, 9 transactions led to an increase in government equity. In 11 transactions, where CPSE was wholly owned by the government, there was no change in government holding. The remaining 16 transactions recorded an average reduction of 1.19% in government equity. In column (2) the count of individual number of CPSEs do not match with the total number of CPSEs because same 8 CPSEs recorded increase in equity in one year while decrease in another (indicated by **).

Table 2: Summary of buyback transactions

Transaction type	Number of transactions	No. of CPSEs	Total disinvestment proceeds(INR million)	Average % of shares sold	Average change in % of govt equity post buyback
Reduction in government holding	16	12	244,947	7.63	(1.19)
Increase in government holding	9	9	83590.7	2.31	0.16
No change in government holding	11	7	75,011	15.55*	0
Total	36	23**	40,3549	8.34	(0.64)
Source : Authors' calculation based on annual reports

Sale to employees

As part of its disinvestment strategy, the government has often reserved a certain quantity of its shares for offer to the CPSE employees. Usually these shares are offered at a discount. Such transactions are expected to incentivise the employees and create dispersed shareholding. In the last six years, there have been 21 such transactions across 15 firms from which the government raised a total of INR 9,379 million. On an average, the % of shares sold to the employees is around 0.14%. Almost half of the proceeds from this method comes from two transactions in FY17 by Indian Oil Corporation Ltd. and NTPC Ltd. In May 2016, government sold 0.29% of the total shares of Indian Oil Corporation Ltd. to its employees raising INR 2,624 million. Pursuant to the 5% OFS stake in February 2016, NTPC offered to sell 2.06 crores equity shares of government to the employees at a discount rate of 5%. 85% of the shares were subscribed by around 10,800 eligible employees and government raised approximately INR 2,037 million.

Exchange traded funds (ETFs)

ETF is a pool of stocks that reflects the composition of an index, like S&P BSE SENSEX. This method has been frequently used for disinvestment in the recent past, where the government sells shareholding in select CPSEs to a fund house which owns the ETF. The ETF fund manager first formulates the scheme and offers to the public for subscription by way of a new fund offer (NFO). The subscription proceeds are used to purchase the shares of constituent companies in similar composition and weights based on the underlying index. Shares are usually sold at a discount to the scheme and the fund manager in turn creates and allots units of the scheme, to the investors. Once the NFO closes, the units are listed on the exchanges.

The government has launched two ETFs, namely, CPSE ETF and Bharat-22 ETF. CPSE ETF was launched in 2014. It contains stock of 11 listed CPSEs and follows the NIFTY CPSE index. In 2017, Bharat-22 ETF was created. This comprises of 16 CPSEs, 3 public sector banks and 3 private company stocks held by Specified Undertaking of the Unit Trust of India (SUUTI). The underlying index is the S&P Bharat 22 index. From FY15 to FY20, there were six tranches of CPSE ETF and four tranches of Bharat-22 ETF transactions which raised INR 989,490 million.

Table 3 lists each ETF tranche from FY15 to FY20 and provides details on allotment date, number of constituent CPSEs, amount raised by government and average reduction in % of government equity post each tranche. It is important to note that the average % reduction in government equity for three ETF transactions in FY20 is unavailable since annual report for the year is not published yet (indicated by *NA).

Table 3: Summary of ETF tranches from FY15 to FY20

ETF Name	ETF tranche	No. of constituent CPSEs	Allotment date of ETF units	Average % reduction in government equity	Amount realised (in INR million)
CPSE ETF	FURTHER FUND OFFER 1	10	28/01/2017	0.98	59999.9
CPSE ETF	FURTHER FUND OFFER 2	10	25/03/2017	0.39	24999.9
CPSE ETF	FURTHER FUND OFFER 3	11	07/12/2018	2.88	170000
CPSE ETF	FURTHER FUND OFFER 4	11	29/03/2019	1.22	93500.7
CPSE ETF	FURTHER FUND OFFER 5	10	26/07/2019	NA*	100003.9
CPSE ETF	FURTHER FUND OFFER 6	10	07/02/2020	NA*	165000
BHARAT 22-ETF	NEW FUND OFFER	16	24/11/2017	0.93	145000
BHARAT 22-ETF	FURTHER FUND OFFER 1	16	29/06/2018	0.58	83252.6
BHARAT 22-ETF	TAP OFFER	16	22/02/2019	0.92	104045.9
BHARAT 22-ETF	FURTHER FUND OFFER 2	16	10/10/2019	NA*	43688
Source : Author's calculation based on annual reports

While aggregate proceeds from ETF may have been high, the average reduction in government equity has been low.

CPSE to CPSE sale

Under this method, government transfers its shares in one CPSE to another CPSE. There have been eight such transactions in the last six years which raised a total of approximately INR 667,119 million. The details of each of the transaction is given in table 4. Except REC Limited, the entire government shareholding was transferred to another CPSE. In case of REC Limited, government still holds 0.25% shares. Post these sales, the firms became subsidiaries of the buyer CPSE firms, but continue to remain government companies as defined under section 2(45) of the Companies Act, 2013.

Table 4: CPSE to CPSE sales from FY15 to FY20

CPSE	Date of transaction	Buyer's Name	% of shares sold	Amount realised (in INR million)
HINDUSTAN PETROLEUM CORPN. LTD.	31/01/2018	OIL & NATURAL GAS CORP.LTD.	51.11	369,150
H S C C (INDIA) LTD.	06/11/2018	NBCC (INDIA) LTD.	100	2,850
DREDGING CORPN. OF INDIA LTD.	09/03/2019	CONSORTIUM OF FOUR PORTS	73.47	10,491
R E C LTD.	28/03/2019	POWER FINANCE CORP.LTD.	52.63	145,000
KAMARAJAR PORT LTD.	27/03/2020	CHENNAI PORT TRUST	66.67	23,830
NORTH EASTERN ELECTRIC POWER CORPN. LTD.	27/03/2020	NTPC LTD.	100	40,000
T H D C INDIA LTD.	27/03/2020	NTPC LTD.	74.49	750,00
NATIONAL PROJECTS CONSTRUCTION CORPN. LTD.	26/04/2020	WAPCOS LTD.	98.89	798
Source : BSEPSU disinvestment database

The CPSE to CPSE sale transactions constituted around 22% of total disinvestment proceeds in the last six years. While technically, the government may have divested 77% shareholding in these CPSEs (as shown in Table 1), it did not bring any change in government ownership of these firms.

Conclusion

There has been a huge increase in disinvestment proceeds in the recent years. However, reduction in government equity in the CPSEs has not witnessed much growth. About 5.19% of disinvestment proceeds came from buyback transactions that led to an increase (or no change) in government equity and 21.8% came from CPSE to CPSE sale transactions that led to no change in government ownership. While 32.2% of proceeds came from public offer, almost 39% of these were actually purchased by LIC. Thus, purchases by LIC accounted for 12.49% of the total proceeds which also imply no change in government ownership. Finally, around 32.4% came from ETFs which, on an average reduced government equity by 1.09%. These considerations become central issues for any research on disinvestment and its impact.

The authors are researchers at NIPFP. We thank Karthik Suresh and Sarang Moharir for useful comments.

Covid-19 and Corporate India

by Aakriti Mathur and Rajeswari Sengupta.

India is dealing with a massive shock in the form of the Covid-19 pandemic. The first case was reported in India on 30 January, 2020. By middle of March the disease had begun spreading rapidly across the country. To prevent the spread of the virus the Indian government announced a nationwide lockdown on 24 March. The pandemic and the lockdown affected nearly all firms and sectors of the economy; however, there are likely to be significant heterogeneities.

We propose a novel approach to identify firms that may have had greater exposure to the pandemic even before it assumed serious proportions in India, by virtue of, for example, their connections to other affected countries, among others. These firms may have fared worse when the lockdown was announced. We also examine the pre-pandemic balance sheet characteristics that may have worsened the impact of the lockdown on some firms compared to the others.

Analysing earnings call reports

We propose the use of earnings call transcripts as an important source of information for gauging a firm's fundamental exposure to the pandemic.

Earnings calls typically follow the presentation of a firm's quarterly results. These calls are attended by senior management of the firm (for example, the CEO, CFO, MD, etc), who present short prepared remarks, and then open the floor to questions from analysts. This implies that the calls are more spontaneous as compared to say the firm's annual report, because the senior management answers questions on the fly from the audience. These reports therefore convey not just fundamental financial information, but also analysts' and managers' opinion about the firm (Borochin et al., 2018).

A significant part of the literature focuses on the tone and sentiment of these reports and their implications for stock market returns, trading volumes (Frankel et al., 1999; Bushee et al., 2003, 2004; Brown et al., 2004), and options pricing (Borochin et al., 2018). Our work closely relates to two recent papers, Hassan et al (2020) and Ramelli and Wagner (2020). Both these studies use the information contained in earnings call transcripts. Hassan et al (2020) focus on globally listed firms, and study whether firms that were more exposed to previous disease outbreaks such as SARS and MERS were better prepared for the 2020 pandemic, and therefore had higher equity returns, than those who were not. Ramelli and Wagner (2020) analyse characteristics of US firms that explain both their stock market performance between January and March 2020 and their discussions of Covid-19 in the earnings transcripts.

Unlike these papers, we use the information in earnings call reports, to measure fundamental exposure of Indian firms to the pandemic. We are interested in using this information to study the equity market performance of the firms around the largest, most stringent lockdown announced in the world (at the time). Our analysis complements earlier work by Sane and Sharma (2020) who calculated the liquidity cover of listed firms in India in the face of large revenue shocks during the pandemic and Bansal et al. (2020) who also examine variations in the market valuation of firms on account of firm-specific characteristics during the pandemic. In this work, we take a more holistic view of firm-level vulnerabilities, examining the fundamental exposure to the pandemic, as well as the role of financial flexibilities, including liquidity. We also focus on one specific event -- the 24 March lockdown -- in order to obtain greater precision.

We focus on earnings calls conducted by firms in January and February 2020, when the case load was still low in India but the pandemic had begun spreading in other countries. These are calls discussing the income statements of October-December, 2019 (Q3 FY20) and January-March, 2020 (Q4 FY20) respectively, of the Indian financial year.

When India reported its first case of the Covid-19 pandemic on 30 January, 2020, close to 7,700 people had been infected all over the world, the majority being in China. Other countries such as the US, Australia, Germany, Japan, South Korea, UAE and HongKong had started reporting Covid-19 cases. By end February, it had morphed into a full blown public health crisis. The total number of infections globally had risen to more than 83,000, with a death toll of more than 2,800. While the disease was spreading rapidly in countries such as Italy, South Korea, France, the US and Iran, these were still early days of the pandemic in India which had less than 10 confirmed cases.

Focusing on the call reports of Jan-Feb 2020 enables us to analyse the firms' exposure to the pandemic at a time when the disease was still at a nascent stage in India unlike say March when the spread of the pandemic had begun affecting most firms. It also allows for easier identification of firm exposure because it is not muddled by domestic policy interventions. For example, there were only 13 Covid-19 related notifications issued by the Indian government in February, compared to 266 in March, as listed by PRS Legislative Research. Hence, from March onwards, the stock market performance of all firms was likely to be affected by these interventions over and above firm-specific concerns around the disease itself.

We start with a sample of the largest listed firms on the Nifty500 index of the National Stock Exchange (NSE) of India. Of the 500 firms in the index, we have access to the call reports of 196 firms in January-February 2020, and of 90 firms in April-May 2020.

Which firms had exposure to Covid-19 in Jan-Feb 2020?

We interpret the number of times a firm mentioned Covid-19 related words in its call reports as an indicator of its exposure to the pandemic. Accordingly, we count the number of times Covid-19 and related words (such as "coronavirus", "pandemic", "ncov", "sarscov", "epidemic" etc) are mentioned in the quarterly earnings call reports of the 196 firms in January-February 2020, and also of the 90 firms in April-May 2020 for the sake of comparison. We briefly summarise our findings below.

• Only one-third of the firms in our Jan-Feb sample mention Covid-19 or related words. The average number of times these words are mentioned is three. Only three of the firms discussing the pandemic are in the financial services sector.
• All 90 firms in the Apr-May 2020 sample mention Covid-19 or related words, demonstrating the extensive spread of the disease by this time. The average occurrence of the words per report is ten times higher, close to 31. This reflects our earlier concern that from March onwards, all firms had become exposed to the pandemic.
• Even in Jan-Feb 2020, there were sector-wise heterogeneities in Covid-19 discussions, as shown in figure 1 below.
• The occurrences of Covid-19 related words were higher in those sectors which presumably have more fundamental exposure to the pandemic, for example in the form of supply-chains with China or other early-affected countries. Some of these sectors are pharmaceuticals, consumer goods, automobile, chemicals etc.
• Firms in health care services, financial services, media and entertainment, power and telecom industries either did not mention or mentioned much less pandemic related words during this period in their call reports. With the possible exception of health care, these sectors were likely to be affected due to indirect exposure to the pandemic.

Figure 1: Sector-wise occurrences of Covid-19 related words in Jan-Feb call reports

We also look at the firms that mentioned "supply", "demand" and "uncertainty" related words in context of the Covid-19 discussion in their call reports. These are likely to be the most common channels of disruption faced by the firms during the pandemic. In results not reported here we find that firms in sectors with higher than average mentions of Covid-19 related words also had higher than average mentions of "supply" related words in the sentences where Covid-19 was discussed. For firms in the services sector, mentions of "demand" related words in the context of the disease were higher. For all the sectors having higher than average mentions of Covid-19 related words, we also find significantly higher mentions of "uncertainty" related words in the context of the pandemic.

This preliminary analysis gives us an idea of which firms and sectors had greater exposure to the pandemic as early as Jan-Feb 2020 when the disease still hadn't spread in India.

For subsequent analysis, we consider the firms that mention Covid-19 in Jan-Feb 2020 as our "treated" sample and those that did not discuss the pandemic as our "control" sample. A relevant question to ask is how similar are the "treated" and "control" samples in terms of their key balance sheet characteristics. Using annual data from the pre-pandemic period (ending in March 2019) from the Prowess database of CMIE, we compare the two sets of firms in size, age, profit, foreign exchange earnings, inventories, cash balances etc. For ease of comparison, we drop the three firms that are in the financial services sector and that mentioned pandemic related words in the Jan-Feb call reports. As shown in table 1 below, we do not find any major difference between these two groups of firms, except that the "treated" firms are on average older and hold higher inventories than the "control" group firms.

Table 1: Summary statistics for non-financial firms: Data as of March 31, 2019

	No. of firms with no COVID mentions in Jan-Feb 2020	No. of firms with COVID mentions in Jan-Feb 2020
	96	60
Variable	Mean of firms with no COVID mentions	Mean of firms with COVID mentions
Age	35	43.7
Log Size	11.19	11.13
Leverage (Debt/Assets)	0.15	0.16
PBDITA/Total Sales	0.26	0.23
FX Earnings/Total income	0.30	0.27
Cash and Bank balance/Total Assets	0.07	0.05
Trade Receivables/Total Assets	0.14	0.13
Inventories/Total Assets	0.09	0.12
Operating Expenses/Total Income	0.76	0.78

What kind of exposure did firms have to the pandemic in Jan-Feb, 2020?

We next analyse the context within which the firms discussed the pandemic in their call reports, for example, references to supply-chains, demand disruptions, or uncertainty due to the pandemic and so on. This will give us a sense of the kind of exposure the firms may have had to the pandemic in the early part of 2020.

We use the techniques applied in Mathur and Sengupta (2019). For every firm's call report, we first isolate the sentences that contain Covid-19 related words. There are 176 sentences in total for the Jan-Feb 2020 reports. Then, we create a word cloud with the most frequently occurring words in these sentences, after stripping out stop-words and other uninformative words.

The word cloud for Q3 FY19-20 reports is shown in figure 2. The size of each word is directly proportional to its frequency in the sentences. We plot the 50 most frequently occurring words. All the coronavirus related words, which are the most common words in these sentences by construction, are not plotted here for ease of comprehension.

• The words "china" and "impact" occur most frequently indicating that firms were talking about the origin of the coronovirus disease and its effect.
• We also see words related to areas where the impact of the pandemic was potentially anticipated or the expected transmission channels of the disease such as "earnings", "shipping", "pharma", "macro", "supply chain", "trade", "logistics", "imports", "demand", "supply", "prices" etc.

Figure 2: Word clouds of sentences with Covid-19 related words in Jan-Feb call reports

Which firms were more affected by the 24 March lockdown announcement?

The 24 March lockdown in India was regarded as one of the most severe lockdowns in the world, based on data from the Oxford COVID-19 Government Response Tracker. All transport services, except those for essential personnel, were suspended, in addition to all educational, commercial, and private establishments (see here). The lockdown affected all sectors of the Indian economy. The stock market reacted negatively overall. This is not surprising, since stock prices reflect changes in expected future cash flows and/or discount rates. However it is possible that some firms were more affected than the others depending on their exposure to the pandemic as well as pre-pandemic characteristics.

To measure the differential, cross-sectional responses of firms to the lockdown announcement, we use high-frequency stock market data and an event study methodology. We have two main hypotheses.

Our primary hypothesis is that firms that were more exposed to the pandemic and mentioned Covid-19 in their earnings call reports in Jan-Feb 2020 (the "treated" group) fared worse than the "control" group when the lockdown was announced.

• If investors believe that firms who discussed Covid-19 and its implications for their businesses early on in the year are more exposed to the virus, for example due to supply chains with China, or factories in badly-affected countries like Italy, then they would revise their expectations of future profitability downwards in response to the lockdown. Therefore, we would see that treated firms as a whole perform worse than control firms.
• If investors believe that early discussions of the pandemic implied that these firms were better prepared to weather the storm, then their returns would be better than those that seemed to have been caught "off-guard". We hypothesise that the former is likelier than the latter, since it is not clear how firms could have unilaterally prepared for the over-arching extent of the shock (such as to demand disruptions) just a couple of months in advance.

Our second hypothesis is that low-profitability firms with higher share of foreign exchange earnings, higher share of inventories, greater dependence on trade credit and higher operating expenses should have witnessed lower stock market returns when the lockdown was announced, compared to more domestically oriented firms which were more profitable, were holding lower inventories, had lower dependence on trade credit and also lower operating expenses.

Estimation strategy

We use a difference-in-difference strategy to estimate the impact of the lockdown event on firms' stock market returns. We consider all "treated" firms as one group by using a dummy ("Covid dummy"). Our dependent variable is the cumulative abnormal stock market returns (CARs) for each firm over a window of (-1, +2) days around the lockdown event, i.e. between 23 March (Monday) and 26 March (Thursday). To obtain these abnormal returns, we estimate a market model (i.e. controlling for movements in the Nifty50 index), as shown in the equation below, over a period of 81 days prior to March 24. More specifically our window starts 91 days prior to the lockdown and stops 11 days before the lockdown. The model specification is:

$$\text{Daily firm returns}_{firm,t} = \alpha + \beta~\text{Daily Nifty50 returns}_{t} + \epsilon$$

The advantage of using a tight window around the event is that it better accounts for anticipation effects and other confounding factors. We use a cross-sectional ordinary least squares regression shown in the equation below, to regress the firm-specific CARs on the "Covid dummy" and on a host of balance sheet variables. Among the regressors, of particular interest is the "Covid dummy" which tells us the difference in CARs between the "treated" and the "control" firms. Other regressors include the balance sheet variables shown in table 1 above as well as dummy variables for the sectors that the firms belong to. Firm level annual balance sheet variables are as of March 31, 2019.

$$\text{CARs around event window}_{firm} = \alpha_{sector} + \beta~\text{Covid Dummy}_{firm} + \\ \log(age)_{firm} + \log(size)_{firm} + Controls_{firm} + \epsilon $$

Results

We summarise our results in Figure 3. In panel (a) we plot the results from our baseline model (model 1) which includes only age and size of firms, and the sector dummies, over and above the Covid dummy. We also plot the results from models 2 to 6 where in addition to the Covid dummy, age, size, and sectors, we sequentially add the regressors of interest: profit, FX earnings, inventories, operating expenses, and trade receivables. We also investigate the role of cash, leverage, borrowing composition, and tangible assets. Here we only report results that are significant at 90% confidence interval. We list our main findings below.

• In all our specifications, the stock returns of "treated" firms, i.e. those that mentioned Covid-19 in their call reports early on in 2020, significantly underperform (at the 90% confidence level or more) the "control" firms. On average, returns of the "treated" firms are roughly 3.5 percentage points lower.
• We find that the equity returns of more profitable firms outperformed those of less profitable ones by 9 percentage points (model 3). Higher profitability implies higher ability to withstand large revenue shortfalls.
• Firms with a higher share of foreign exchange earnings in their total income performed worse. They were likely to be more affected due to supply and demand disruptions in the rest of the world.
• Firms with high inventories saw 24 percentage points lower returns. High share of inventories in total assets might make it difficult for firms to get rid of their inventories once an economywide lockdown is announced yet they would have had to incur the costs of maintaining these inventories which makes them worse off than firms with lower share of inventories (Banerjee et al., 2020).
• Firms with higher pre-pandemic trade credit reliance saw significantly lower abnormal returns. This is likely because in a broad-based crisis such as this one, credit markets are likely to freeze along both extensive and intensive margins. Thus, rolling over existing trade credit as well as obtaining new supply of trade credit would be difficult. (Banerjee et al., 2020).
• Firms with higher pre-pandemic operating expenses also fared worse once the lockdown was announced. Operating expenses are typically short term expenses. In absence of steady revenues in a lockdown, firms would depend on credit from the financial system to meet these expenses. During a crisis if the financial system is unwilling to offer short term credit (Sengupta and Vardhan, 2020), then these firms are likely to witness lower stock returns.

In figure 3, panel (b), we plot the coefficients on the sector dummies from the baseline model 1, with only age and size included as controls. Automobiles is the benchmark sector. We find that stock returns of more consumer facing sectors (textiles, media and entertainment) and those that rely on supply chains (metals, and oil and gas) did particularly badly when the lockdown was announced. On the other hand, healthcare services in particular outperformed as compared to automobiles.

Figure 3, panel (A): Explaining cumulative abnormal returns around first lockdown (24 March, 2020)

Figure 3, panel (B): Sector dummies from baseline regression

In a nutshell, we find that when the nationwide lockdown was announced on 24 March, firms who mentioned Covid-19 in their earnings calls in early-2020 and hence were more exposed to the pandemic, fared worse than firms who did not discuss the pandemic. This result holds when we account for the sectors and key balance sheet characteristics of the firms.

As discussed in Fahlenbrach et al.(2020), less financially flexible firms are less able to withstand large negative shocks to their revenues, which translates to worse equity market performance. Lower cash, lower profitability, lower diversification in earnings (e.g. higher reliance on foreign exchange revenues) or in borrowing sources (e.g. higher reliance on trade credit) can all be considered indicators of low financial flexibility. In other words, firms that had lower financial flexibility in the pre-pandemic period were worse affected when the lockdown was announced.

We further find that controlling for mentions of "supply" and "demand" related words (not shown here) in the firms' call reports -- which may account for the nature of their exposure to the pandemic -- does not change the results qualitatively, and makes them stronger in some specifications.

Firms with more cash holdings reported higher returns on average around the lockdown announcement, but this effect is not significant (hence, not reported here). In further tests, we find some evidence of non-linearities. Firms with above-median cash holdings significantly outperform their counterpart.

Conclusion

Using the informational content of earnings call reports of some of the largest, non-financial firms in India we throw light on the firms and sectors that may have been more exposed to the pandemic as early as January and February 2020 when as per the official statistics, the disease had still not spread in India. We find that these firms were also worse affected by the announcement of a nationwide lockdown in March compared to firms that were presumably less exposed to the pandemic early on.

Our results highlight the kind of firms that are likely to be more affected when a crisis such as the ongoing one hits the economy. Firms with lower profits, higher share of foreign exchange earnings, higher share of inventories, greater dependence on trade credit and higher operating expenses fared worse on the stock market when the lockdown was announced.

References

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Aakriti Mathur is a PhD candidate at The Graduate Institute (IHEID), Geneva. Rajeswari Sengupta is an Assistant Professor of Economics at IGIDR, Mumbai.